OpenRaiser/Intern-Atlas huggingface dataset:
Optimizing Workflow for Elite Developers: Perspectives on Leveraging SE Bots
Venue: arXiv cs.SE 2023
Year: 2023

Small-scale automation services in Software Engineering, known as SE Bots, have gradually infiltrated every aspect of daily software development with the goal of enhancing productivity and well-being. While leading the OSS development, elite developers have often burned out from holistic responsibilities in projects and looked for automation support. Building on prior research in BotSE and our interviews with elite developers, this paper discusses how to design and implement SE bots that integrate into the workflows of elite developers and meet their expectations. We present six main design guidelines for implementing SE bots for elite developers, based on their concerns about noise, security, simplicity, and other factors. Additionally, we discuss the future directions of SE bots, especially in supporting elite developers' increasing workload due to rising demands.
Sampling-based Path Planning Algorithms: A Survey
Venue: arXiv cs.RO 2023
Year: 2023

Path planning is a classic problem for autonomous robots. To ensure safe and efficient point-to-point navigation an appropriate algorithm should be chosen keeping the robot's dimensions and its classification in mind. Autonomous robots use path-planning algorithms to safely navigate a dynamic, dense, and unknown environment. A few metrics for path planning algorithms to be taken into account are safety, efficiency, lowest-cost path generation, and obstacle avoidance. Before path planning can take place we need map representation which can be discretized or open configuration space. Discretized configuration space provides node/connectivity information from one point to another. While in open/free configuration space it is up to the algorithm to create a list of nodes and then find a feasible path. Both types of maps are populated by obstacle positions using perception obstacle detection techniques to represent current obstacles from the perspective of the robot. For open configuration spaces, sampling-based planning algorithms are used. This paper aims to explore various types of Sampling-based path-planning algorithms such as Probabilistic RoadMap (PRM), and Rapidly-exploring Random Trees (RRT). These two algorithms also have optimized versions - PRM* and RRT* and this paper discusses how that optimization is achieved and is beneficial.
Using Large Language Models for Interpreting Autonomous Robots Behaviors
Venue: arXiv cs.RO 2023
Year: 2023

The deployment of autonomous robots in various domains has raised significant concerns about their trustworthiness and accountability. This study explores the potential of Large Language Models (LLMs) in analyzing ROS 2 logs generated by autonomous robots and proposes a framework for log analysis that categorizes log files into different aspects. The study evaluates the performance of three different language models in answering questions related to StartUp, Warning, and PDDL logs. The results suggest that GPT 4, a transformer-based model, outperforms other models, however, their verbosity is not enough to answer why or how questions for all kinds of actors involved in the interaction.
Wasserstein Dictionaries of Persistence Diagrams
Venue: arXiv cs.LG 2023
Year: 2023

This paper presents a computational framework for the concise encoding of an ensemble of persistence diagrams, in the form of weighted Wasserstein barycenters [100], [102] of a dictionary of atom diagrams. We introduce a multi-scale gradient descent approach for the efficient resolution of the corresponding minimization problem, which interleaves the optimization of the barycenter weights with the optimization of the atom diagrams. Our approach leverages the analytic expressions for the gradient of both sub-problems to ensure fast iterations and it additionally exploits shared-memory parallelism. Extensive experiments on public ensembles demonstrate the efficiency of our approach, with Wasserstein dictionary computations in the orders of minutes for the largest examples. We show the utility of our contributions in two applications. First, we apply Wassserstein dictionaries to data reduction and reliably compress persistence diagrams by concisely representing them with their weights in the dictionary. Second, we present a dimensionality reduction framework based on a Wasserstein dictionary defined with a small number of atoms (typically three) and encode the dictionary as a low dimensional simplex embedded in a visual space (typically in 2D). In both applications, quantitative experiments assess the relevance of our framework. Finally, we provide a C++ implementation that can be used to reproduce our results.
Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability
Venue: arXiv cs.AI 2023
Year: 2023

Analysis of how semantic concepts are represented within Convolutional Neural Networks (CNNs) is a widely used approach in Explainable Artificial Intelligence (XAI) for interpreting CNNs. A motivation is the need for transparency in safety-critical AI-based systems, as mandated in various domains like automated driving. However, to use the concept representations for safety-relevant purposes, like inspection or error retrieval, these must be of high quality and, in particular, stable. This paper focuses on two stability goals when working with concept representations in computer vision CNNs: stability of concept retrieval and of concept attribution. The guiding use-case is a post-hoc explainability framework for object detection (OD) CNNs, towards which existing concept analysis (CA) methods are successfully adapted. To address concept retrieval stability, we propose a novel metric that considers both concept separation and consistency, and is agnostic to layer and concept representation dimensionality. We then investigate impacts of concept abstraction level, number of concept training samples, CNN size, and concept representation dimensionality on stability. For concept attribution stability we explore the effect of gradient instability on gradient-based explainability methods. The results on various CNNs for classification and object detection yield the main findings that (1) the stability of concept retrieval can be enhanced through dimensionality reduction via data aggregation, and (2) in shallow layers where gradient instability is more pronounced, gradient smoothing techniques are advised. Finally, our approach provides valuable insights into selecting the appropriate layer and concept representation dimensionality, paving the way towards CA in safety-critical XAI applications.
The Power of Typed Affine Decision Structures: A Case Study
Venue: arXiv cs.LG 2023
Year: 2023

TADS are a novel, concise white-box representation of neural networks. In this paper, we apply TADS to the problem of neural network verification, using them to generate either proofs or concise error characterizations for desirable neural network properties. In a case study, we consider the robustness of neural networks to adversarial attacks, i.e., small changes to an input that drastically change a neural networks perception, and show that TADS can be used to provide precise diagnostics on how and where robustness errors a occur. We achieve these results by introducing Precondition Projection, a technique that yields a TADS describing network behavior precisely on a given subset of its input space, and combining it with PCA, a traditional, well-understood dimensionality reduction technique. We show that PCA is easily compatible with TADS. All analyses can be implemented in a straightforward fashion using the rich algebraic properties of TADS, demonstrating the utility of the TADS framework for neural network explainability and verification. While TADS do not yet scale as efficiently as state-of-the-art neural network verifiers, we show that, using PCA-based simplifications, they can still scale to mediumsized problems and yield concise explanations for potential errors that can be used for other purposes such as debugging a network or generating new training samples.
A technological framework for scalable ground-up formation of Circular Societies
Venue: arXiv cs.CY 2023
Year: 2023

The Circular Economy (CE) is regarded as a solution to the environmental crisis. However, mainstream CE measures skirt around challenging the ethos of ever-increasing economic growth, overlooking social impacts and under-representing solutions such as reducing overall consumption. Circular Societies (CS) address these concerns by challenging this ethos. They emphasize ground-up social reorganization,address over-consumption through sufficiency strategies, and highlight the need for considering the complex inter-dependencies between nature, society, and technology on local, regional and global levels. However, no blueprint exists for forming CSs. An initial objective of my thesis is exploring existing social-network ontologies and developing a broadly applicable model for CSs. Since ground-up social reorganization on local, regional, and global levels has compounding effects on network complexities,a technological framework digitizing these inter-dependencies is necessary. Finally, adhering to CS principles of transparency and democratization, a system of trust is necessary to achieve collaborative consensus of the network state.
An Edge Assisted Robust Smart Traffic Management and Signalling System for Guiding Emergency Vehicles During Peak Hours
Venue: arXiv cs.CV 2023
Year: 2023

Congestion in traffic is an unavoidable circumstance in many cities in India and other countries. It is an issue of major concern. The steep rise in the number of automobiles on the roads followed by old infrastructure, accidents, pedestrian traffic, and traffic rule violations all add to challenging traffic conditions. Given these poor conditions of traffic, there is a critical need for automatically detecting and signaling systems. There are already various technologies that are used for traffic management and signaling systems like video analysis, infrared sensors, and wireless sensors. The main issue with these methods is they are very costly and high maintenance is required. In this paper, we have proposed a three-phase system that can guide emergency vehicles and manage traffic based on the degree of congestion. In the first phase, the system processes the captured images and calculates the Index value which is used to discover the degree of congestion. The Index value of a particular road depends on its width and the length up to which the camera captures images of that road. We have to take input for the parameters (length and width) while setting up the system. In the second phase, the system checks whether there are any emergency vehicles present or not in any lane. In the third phase, the whole processing and decision-making part is performed at the edge server. The proposed model is robust and it takes into consideration adverse weather conditions such as hazy, foggy, and windy. It works very efficiently in low light conditions also. The edge server is a strategically placed server that provides us with low latency and better connectivity. Using Edge technology in this traffic management system reduces the strain on cloud servers and the system becomes more reliable in real-time because the latency and bandwidth get reduced due to processing at the intermediate edge server.
Dynamic Tracing: a graphical language for rewriting protocols
Venue: arXiv cs.LO 2023
Year: 2023

The category Set_* of sets and partial functions is well-known to be traced monoidal, meaning that a partial function S+U -/-> T+U can be coherently transformed into a partial function S -/-> T. This transformation is generally described in terms of an implicit procedure that must be run. We make this procedure explicit by enriching the traced category in Cat#, the symmetric monoidal category of categories and cofunctors: each hom-category has such procedures as objects, and advancement through the procedures as arrows. We also generalize to traced Kleisli categories beyond Set_*, providing a conjectural trace operator for the Kleisli category of any polynomial monad of the form t+1. The main motivation for this work is to give a formal and graphical syntax for performing sophisticated computations powered by graph rewriting, which is itself a graphical language for data transformation.
PAO: A general particle swarm algorithm with exact dynamics and closed-form transition densities
Venue: arXiv cs.NE 2023
Year: 2023

A great deal of research has been conducted in the consideration of meta-heuristic optimisation methods that are able to find global optima in settings that gradient based optimisers have traditionally struggled. Of these, so-called particle swarm optimisation (PSO) approaches have proven to be highly effective in a number of application areas. Given the maturity of the PSO field, it is likely that novel variants of the PSO algorithm stand to offer only marginal gains in terms of performance -- there is, after all, no free lunch. Instead of only chasing performance on suites of benchmark optimisation functions, it is argued herein that research effort is better placed in the pursuit of algorithms that also have other useful properties. In this work, a highly-general, interpretable variant of the PSO algorithm -- particle attractor algorithm (PAO) -- is proposed. Furthermore, the algorithm is designed such that the transition densities (describing the motions of the particles from one generation to the next) can be computed exactly in closed form for each step. Access to closed-form transition densities has important ramifications for the closely-related field of Sequential Monte Carlo (SMC). In order to demonstrate that the useful properties do not come at the cost of performance, PAO is compared to several other state-of-the art heuristic optimisation algorithms in a benchmark comparison study.
ChatGPT in the Classroom: An Analysis of Its Strengths and Weaknesses for Solving Undergraduate Computer Science Questions
Venue: arXiv cs.HC 2023
Year: 2023

ChatGPT is an AI language model developed by OpenAI that can understand and generate human-like text. It can be used for a variety of use cases such as language generation, question answering, text summarization, chatbot development, language translation, sentiment analysis, content creation, personalization, text completion, and storytelling. While ChatGPT has garnered significant positive attention, it has also generated a sense of apprehension and uncertainty in academic circles. There is concern that students may leverage ChatGPT to complete take-home assignments and exams and obtain favorable grades without genuinely acquiring knowledge. This paper adopts a quantitative approach to demonstrate ChatGPT's high degree of unreliability in answering a diverse range of questions pertaining to topics in undergraduate computer science. Our analysis shows that students may risk self-sabotage by blindly depending on ChatGPT to complete assignments and exams. We build upon this analysis to provide constructive recommendations to both students and instructors.
Topologically sorting VDM-SL definitions for Isabelle/HOL translation
Venue: arXiv cs.PL 2023
Year: 2023

There is an ecosystem of VDM libraries and extensions that includes a translation and proof environment for VDM in Isabelle. Translation works for a large subset of VDM-SL and further constructs are being added on demand. A key impediment for novice users is that Isabelle/HOL requires all definitions to be declared before they are used, where (mutually) recursive definitions must be defined in tandem. In this paper, we describe a solution to this problem, which will enable wider access to the translator plugin for novice users as well as real models.
Servo: Increasing the Scalability of Modifiable Virtual Environments Using Serverless Computing -- Extended Technical Report
Venue: arXiv cs.DC 2023
Year: 2023

Online games with modifiable virtual environments (MVEs) have become highly popular over the past decade. Among them, Minecraft -- supporting hundreds of millions of users -- is the best-selling game of all time, and is increasingly offered as a service. Although Minecraft is architected as a distributed system, in production it achieves this scale by partitioning small groups of players over isolated game instances. From the approaches that can help other kinds of virtual worlds scale, none is designed to scale MVEs, which pose a unique challenge -- a mix between the count and complexity of active in-game constructs, player-created in-game programs, and strict quality of service. Serverless computing emerged recently and focuses, among others, on service scalability. Thus, addressing this challenge, in this work we explore using serverless computing to improve MVE scalability. To this end, we design, prototype, and evaluate experimentally Servo, a serverless backend architecture for MVEs. We implement Servo as a prototype and evaluate it using real-world experiments on two commercial serverless platforms, of Amazon Web Services (AWS) and Microsoft Azure. Results offer strong support that our serverless MVE can significantly increase the number of supported players per instance without performance degradation, in our key experiment by 40 to 140 players per instance, which is a significant improvement over state-of-the-art commercial and open-source alternatives. We release Servo as open-source, on Github: https://github.com/atlarge-research/opencraft
Explainable Verbal Reasoner Plus (EVR+): A Natural Language Reasoning Framework that Supports Diverse Compositional Reasoning
Venue: arXiv cs.CL 2023
Year: 2023

Languages models have been successfully applied to a variety of reasoning tasks in NLP, yet the language models still suffer from compositional generalization. In this paper we present Explainable Verbal Reasoner Plus (EVR+), a reasoning framework that enhances language models' compositional reasoning ability by (1) allowing the model to explicitly generate and execute symbolic operators, and (2) allowing the model to decompose a complex task into several simpler ones in a flexible manner. Compared with its predecessor Explainable Verbal Reasoner (EVR) and other previous approaches adopting similar ideas, our framework supports more diverse types of reasoning such as nested loops and different types of recursion. To evaluate our reasoning framework, we build a synthetic dataset with five tasks that require compositional reasoning. Results show that our reasoning framework can enhance the language model's compositional generalization performance on the five tasks, using a fine-tuned language model. We also discussed the possibility and the challenges to combine our reasoning framework with a few-shot prompted language model.
Logic-based similarity
Venue: arXiv cs.LO 2023
Year: 2023

This paper develops a {\em qualitative} and logic-based notion of similarity from the ground up using only elementary concepts of first-order logic centered around the fundamental model-theoretic notion of type.
Wearing face mask detection using deep learning through COVID-19 pandemic
Venue: arXiv cs.CV 2023
Year: 2023

During the COVID-19 pandemic, wearing a face mask has been known to be an effective way to prevent the spread of COVID-19. In lots of monitoring tasks, humans have been replaced with computers thanks to the outstanding performance of the deep learning models. Monitoring the wearing of a face mask is another task that can be done by deep learning models with acceptable accuracy. The main challenge of this task is the limited amount of data because of the quarantine. In this paper, we did an investigation on the capability of three state-of-the-art object detection neural networks on face mask detection for real-time applications. As mentioned, here are three models used, Single Shot Detector (SSD), two versions of You Only Look Once (YOLO) i.e., YOLOv4-tiny, and YOLOv4-tiny-3l from which the best was selected. In the proposed method, according to the performance of different models, the best model that can be suitable for use in real-world and mobile device applications in comparison to other recent studies was the YOLOv4-tiny model, with 85.31% and 50.66 for mean Average Precision (mAP) and Frames Per Second (FPS), respectively. These acceptable values were achieved using two datasets with only 1531 images in three separate classes.
HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classification
Venue: arXiv cs.CL 2023
Year: 2023

We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) task, a shared task on offensive language (sexism) detection on English Gab and Reddit dataset. We investigated the effects of transferring two language models: XLM-T (sentiment classification) and HateBERT (same domain -- Reddit) for multi-level classification into Sexist or not Sexist, and other subsequent sub-classifications of the sexist data. We also use synthetic classification of unlabelled dataset and intermediary class information to maximize the performance of our models. We submitted a system in Task A, and it ranked 49th with F1-score of 0.82. This result showed to be competitive as it only under-performed the best system by 0.052% F1-score.
Exploiting the Distortion-Semantic Interaction in Fisheye Data
Venue: arXiv cs.CV 2023
Year: 2023

In this work, we present a methodology to shape a fisheye-specific representation space that reflects the interaction between distortion and semantic context present in this data modality. Fisheye data has the wider field of view advantage over other types of cameras, but this comes at the expense of high radial distortion. As a result, objects further from the center exhibit deformations that make it difficult for a model to identify their semantic context. While previous work has attempted architectural and training augmentation changes to alleviate this effect, no work has attempted to guide the model towards learning a representation space that reflects this interaction between distortion and semantic context inherent to fisheye data. We introduce an approach to exploit this relationship by first extracting distortion class labels based on an object's distance from the center of the image. We then shape a backbone's representation space with a weighted contrastive loss that constrains objects of the same semantic class and distortion class to be close to each other within a lower dimensional embedding space. This backbone trained with both semantic and distortion information is then fine-tuned within an object detection setting to empirically evaluate the quality of the learnt representation. We show this method leads to performance improvements by as much as 1.1% mean average precision over standard object detection strategies and .6% improvement over other state of the art representation learning approaches.
AVATAR: Adversarial self-superVised domain Adaptation network for TARget domain
Venue: arXiv cs.CV 2023
Year: 2023

This paper presents an unsupervised domain adaptation (UDA) method for predicting unlabeled target domain data, specific to complex UDA tasks where the domain gap is significant. Mainstream UDA models aim to learn from both domains and improve target discrimination by utilizing labeled source domain data. However, the performance boost may be limited when the discrepancy between the source and target domains is large or the target domain contains outliers. To explicitly address this issue, we propose the Adversarial self-superVised domain Adaptation network for the TARget domain (AVATAR) algorithm. It outperforms state-of-the-art UDA models by concurrently reducing domain discrepancy while enhancing discrimination through domain adversarial learning, self-supervised learning, and sample selection strategy for the target domain, all guided by deep clustering. Our proposed model significantly outperforms state-of-the-art methods on three UDA benchmarks, and extensive ablation studies and experiments demonstrate the effectiveness of our approach for addressing complex UDA tasks.